Institution
Helsinki Institute for Information Technology
Facility•Espoo, Finland•
About: Helsinki Institute for Information Technology is a facility organization based out in Espoo, Finland. It is known for research contribution in the topics: Population & Bayesian network. The organization has 630 authors who have published 1962 publications receiving 63426 citations.
Papers published on a yearly basis
Papers
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TL;DR: A local algorithm for finding a 3-approximate vertex cover in bounded-degree graphs that is deterministic, and no auxiliary information besides port numbering is required.
50 citations
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20 Aug 2005
TL;DR: A critical design agenda that pragmatically joins: concepts from media studies, tangible or ubiquitous media design concerns, anthropological perspectives to performance and practices of theatre performance is proposed.
Abstract: This paper addresses design agendas in Human-Computer Interaction and neighbouring fields motivated by the mixing of areas that were mostly kept separate until recently, such as media studies, performing arts, computing, and ubiquitous or tangible interfaces Referring to new developments in this interdisciplinary research area, and moving from three specific design cases, this paper proposes a critical design agenda that pragmatically joins: concepts from media studies, tangible or ubiquitous media design concerns, anthropological perspectives to performance and practices of theatre performance
50 citations
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26 Jun 2012TL;DR: A fully conjugate Bayesian formulation is proposed and derived, which allows us to combine hundreds or thousands of kernels very efficiently and can be extended for multiclass learning and semi-supervised learning.
Abstract: Multiple kernel learning algorithms are proposed to combine kernels in order to obtain a better similarity measure or to integrate feature representations coming from different data sources. Most of the previous research on such methods is focused on the computational efficiency issue. However, it is still not feasible to combine many kernels using existing Bayesian approaches due to their high time complexity. We propose a fully conjugate Bayesian formulation and derive a deterministic variational approximation, which allows us to combine hundreds or thousands of kernels very efficiently. We briefly explain how the proposed method can be extended for multiclass learning and semi-supervised learning. Experiments with large numbers of kernels on benchmark data sets show that our inference method is quite fast, requiring less than a minute. On one bioinformatics and three image recognition data sets, our method outperforms previously reported results with better generalization performance.
50 citations
01 Jun 2011
TL;DR: A joint generative model of tumor growth and of image observation that naturally handles multimodal and longitudinal data is proposed that can be used for integrating information from different multi-modal imaging protocols and can be adapted to other tumor growth models.
Abstract: 22nd International Conference, IPMI 2011, Kloster Irsee, Germany, July 3-8, 2011. Proceedings
50 citations
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TL;DR: It is shown that if a local algorithm finds a constant-factor approximation of a simple PO-checkable graph problem with the help of unique identifiers, then the same approximation ratio can be achieved on anonymous networks.
Abstract: In the study of deterministic distributed algorithms, it is commonly assumed that each node has a unique O(log n)-bit identifier. We prove that for a general class of graph problems, local algorithms (constant-time distributed algorithms) do not need such identifiers: a port numbering and orientation is sufficient.Our result holds for so-called simple PO-checkable graph optimisation problems; this includes many classical packing and covering problems such as vertex covers, edge covers, matchings, independent sets, dominating sets, and edge dominating sets. We focus on the case of bounded-degree graphs and show that if a local algorithm finds a constant-factor approximation of a simple PO-checkable graph problem with the help of unique identifiers, then the same approximation ratio can be achieved on anonymous networks.As a corollary of our result, we derive a tight lower bound on the local approximability of the minimum edge dominating set problem. By prior work, there is a deterministic local algorithm that achieves the approximation factor of 4--1/⌊Δ/2⌋ in graphs of maximum degree Δ. This approximation ratio is known to be optimal in the port-numbering model—our main theorem implies that it is optimal also in the standard model in which each node has a unique identifier.Our main technical tool is an algebraic construction of homogeneously ordered graphs: We say that a graph is (α,r)-homogeneous if its nodes are linearly ordered so that an α fraction of nodes have pairwise isomorphic radius-r neighbourhoods. We show that there exists a finite (α,r)-homogeneous 2k-regular graph of girth at least g for any α
50 citations
Authors
Showing all 632 results
Name | H-index | Papers | Citations |
---|---|---|---|
Dimitri P. Bertsekas | 94 | 332 | 85939 |
Olli Kallioniemi | 90 | 353 | 42021 |
Heikki Mannila | 72 | 295 | 26500 |
Jukka Corander | 66 | 411 | 17220 |
Jaakko Kangasjärvi | 62 | 146 | 17096 |
Aapo Hyvärinen | 61 | 301 | 44146 |
Samuel Kaski | 58 | 522 | 14180 |
Nadarajah Asokan | 58 | 327 | 11947 |
Aristides Gionis | 58 | 292 | 19300 |
Hannu Toivonen | 56 | 192 | 19316 |
Nicola Zamboni | 53 | 128 | 11397 |
Jorma Rissanen | 52 | 151 | 22720 |
Tero Aittokallio | 52 | 271 | 8689 |
Juha Veijola | 52 | 261 | 19588 |
Juho Hamari | 51 | 176 | 16631 |